Embodiments of the present invention relate generally to control systems and, more particularly, to model predictive techniques for the optimization and management of various process parameters in a paper manufacturing process.
While a diverse number of manufacturing processes currently exist for producing a wide variety of products, these manufacturing processes typically share the common goal of ensuring that a resulting product conforms to certain target quality specifications. In parallel to achieving certain quality targets, many manufacturing processes also share the common goal of ensuring that the cost to achieve target quality is managed or optimized. In controlling a process in this regard, a determination as to whether a resulting product conforms to a particular quality standard may be indicated by one or more quality parameters derived during the process. In some instances, these quality parameters may be measured online directly from the process by using one or more measurement devices, such as sensors, transducers, or the like. Thus, using these “on-process” measured quality parameter values, a control system monitoring the manufacturing process may be able to adjust one or more other process variables in order to maintain the quality parameter at a desired target value (e.g., a set point, range, or maximum, etc.).
In some processes, however, certain parameters, including those relating to production costs, product quality, and various process runtime properties, may not be directly measurable using conventional sensors and measuring devices. For example, in a paper manufacturing process, certain quality properties of the finished paper products may only be determined by taking a sample of the finished paper from the paper machine and performing various destructive tests in a testing setting that is separate from the process, such as in a dedicated or automated testing system, through one or more laboratory-based tests or measurements, or by other offline product testing arrangements (e.g., including offline sensors not in direct communication with the process system). These samples may normally be taken when a reel of finished paper is removed from the paper machine. Thus, these “off-process” measurements may not be available for use in process control purposes during the production of the reel from which the measurement is obtained. Further, due to operation costs and logistics, it is not always feasible or practical to sample each finished reel. As a result, there may only be sporadic off-process measurements of the quality parameter available to a machine operator.
In some instances, control of the paper manufacturing process has been accomplished using manual procedures performed by a machine operator based on the above-mentioned off-process measurement results, as the feedback information required for closed loop control may not be available. However, the use of off-process analysis to obtain such parameters may be problematic because substantially quantities of product, even one or more rolls of paper, may be produced before changes in control based on the off-process test results may be implemented. Additionally, optimization of the paper manufacturing process may require the manual control of various unit operations within the paper manufacturing plant that govern certain specific areas of the paper making process, which may be independent or disconnected from each other (e.g., non-networked; non-integrated controls). Further, even if manual control of a paper quality parameter could be achieved, such control typically does not take into consideration the materials and operational costs associated with production. That is, any manual control or potential optimization of a process may require that the operator consistently adhere to management-defined guidelines relating to the trade off between the quality of the final product as well as the cost of the materials. Accordingly, there exists a need for an improved technique for controlling certain quality parameters in processes that are not directly measurable online.